import pandas as pd
import numpy as np
import plotly.graph_objs as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_white"
data = pd.read_csv("Colorado.csv", encoding='latin-1')
print(data.head())
ID Mountain Peak Mountain Range Elevation_ft fourteener \ 0 1 Mount Elbert Sawatch Range 14440 Y 1 2 Mount Massive Sawatch Range 14428 Y 2 3 Mount Harvard Sawatch Range 14421 Y 3 4 Blanca Peak Sangre de Cristo Range 14351 Y 4 5 La Plata Peak Sawatch Range 14343 Y Prominence_ft Isolation_mi Standard Route Distance_mi \ 0 9093 670.00 Northeast Ridge 9.50 1 1961 5.06 East Slopes 14.50 2 2360 14.93 South Slopes 14.00 3 5326 103.40 Northwest Ridge 17.00 4 1836 6.28 Northwest Ridge 9.25 Elevation Gain_ft Difficulty Traffic Low Traffic High 0 4700 Class 1 20000 25000 1 4500 Class 2 7000 10000 2 4600 Class 2 5000 7000 3 6500 Class 2 1000 3000 4 4500 Class 2 5000 7000
print(data.isnull().sum())
ID 0 Mountain Peak 0 Mountain Range 0 Elevation_ft 0 fourteener 0 Prominence_ft 0 Isolation_mi 0 Standard Route 0 Distance_mi 0 Elevation Gain_ft 0 Difficulty 0 Traffic Low 0 Traffic High 0 dtype: int64
print(data.columns)
Index(['ID', 'Mountain Peak', 'Mountain Range', 'Elevation_ft', 'fourteener',
'Prominence_ft', 'Isolation_mi', 'Standard Route', 'Distance_mi',
'Elevation Gain_ft', 'Difficulty', 'Traffic Low', 'Traffic High'],
dtype='object')
#Select the columns
column1 = data['Elevation_ft']
column2 = data['Elevation Gain_ft']
# Calculate the correlation coefficient
correlation_coefficient = column1.corr(column2)
print("Correlation Coefficient:", correlation_coefficient)
Correlation Coefficient: -0.07298694388781231
fig = go.Figure()
fig.add_trace(go.Box(y=data['Elevation_ft'],
name='Elevation_ft'))
fig.update_layout(title='Elevation_ft Box Plot',
yaxis_title='Elevation_ft')
fig.show()
fig = go.Figure()
fig.add_trace(go.Bar(x=data['Elevation_ft'],
y=data['Difficulty'],
name='Difficulty by Elevation_ft'))
fig.update_layout(title='Difficulty Trend', xaxis_title='Elevation_ft',
yaxis_title=' Difficulty')
fig.show()
import matplotlib.pyplot as plt
data.reset_index().plot(x="Mountain Range",
y="Prominence_ft",
figsize=(15,12), kind="bar",
title = "Prominence_ft by Mountain Range")
plt.style.use('fivethirtyeight')
plt.show()
ratings = data["Difficulty"].value_counts()
numbers = ratings.index
quantity = ratings.values
import plotly.express as px
figure = px.pie(data,
values=quantity,
names=numbers,hole = 0.5)
figure.show()
import seaborn as sns
plt.figure(figsize=(10, 8))
plt.style.use('fivethirtyeight')
plt.title("Distribution of Impressions From Prominence_ft")
sns.distplot(data['Elevation Gain_ft'])
plt.show()
C:\Users\yemiw\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
plt.figure(figsize=(10, 8))
plt.title("Distribution of Impressions From Distance_mi")
sns.distplot(data['Distance_mi'])
plt.show()
C:\Users\yemiw\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
plt.figure(figsize=(10, 8))
plt.title("Distribution of Impressions From Explore")
sns.distplot(data['Isolation_mi'])
plt.show()
C:\Users\yemiw\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
Prominence_ft = data["Prominence_ft"].sum()
Distance_mi = data["Distance_mi"].sum()
Isolation_mi = data["Isolation_mi"].sum()
labels = ['Prominence_ft','Distance_mi','Isolation_mi']
values = [ Prominence_ft , Distance_mi, Isolation_mi]
fig = px.pie(data, values=values, names=labels,
title='Impressions on 14ers From Various Sources', hole=0.5)
fig.show()